CockroachDB: The Definitive Guide
Get the lowdown on CockroachDB, the distributed SQL database built to handle the demands of today's data-driven cloud applications. In this hands-on guide, software developers, architects, and DevOps/SRE teams will learn how to use CockroachDB to create applications that scale elastically and provide seamless delivery for end users while remaining indestructible. Teams will also learn how to migrate existing applications to CockroachDB's performant, cloud native data architecture. If you're familiar with distributed systems, you'll quickly discover the benefits of strong data correctness and consistency guarantees as well as optimizations for delivering ultra low latencies to globally distributed end users. You'll learn how to: Design and build applications for distributed infrastructure, including data modeling and schema design; Migrate data into CockroachDB; Read and write data and run ACID transactions across distributed infrastructure; Plan a CockroachDB deployment for resil ...
Get the lowdown on CockroachDB, the distributed SQL database built to handle the demands of today's data-driven cloud applications. In this hands-on guide, software developers, architects, and DevOps/SRE teams will learn how to use CockroachDB to create applications that scale elastically and provide seamless delivery for end users while remaining indestructible. Teams will also learn how to migrate existing applications to CockroachDB's performant, cloud native data architecture. If you're familiar with distributed systems, you'll quickly discover the benefits of strong data correctness and consistency guarantees as well as optimizations for delivering ultra low latencies to globally distributed end users. You'll learn how to: Design and build applications for distributed infrastructure, including data modeling and schema design; Migrate data into CockroachDB; Read and write data and run ACID transactions across distributed infrastructure; Plan a CockroachDB deployment for resil ...
Distributed Machine Learning with Python
Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the en ...
Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the en ...
Computer Vision Projects with PyTorch
Design and develop end-to-end, production-grade computer vision projects for real-world industry problems. This book discusses computer vision algorithms and their applications using PyTorch. The book begins with the fundamentals of computer vision: convolutional neural nets, RESNET, YOLO, data augmentation, and other regularization techniques used in the industry. And then it gives you a quick overview of the PyTorch libraries used in the book. After that, it takes you through the implementation of image classification problems, object detection techniques, and transfer learning while training and running inference. The book covers image segmentation and an anomaly detection model. And it discusses the fundamentals of video processing for computer vision tasks putting images into videos. The book concludes with an explanation of the complete model building process for deep learning frameworks using optimized techniques with highlights on model AI explainability. After reading th ...
Design and develop end-to-end, production-grade computer vision projects for real-world industry problems. This book discusses computer vision algorithms and their applications using PyTorch. The book begins with the fundamentals of computer vision: convolutional neural nets, RESNET, YOLO, data augmentation, and other regularization techniques used in the industry. And then it gives you a quick overview of the PyTorch libraries used in the book. After that, it takes you through the implementation of image classification problems, object detection techniques, and transfer learning while training and running inference. The book covers image segmentation and an anomaly detection model. And it discusses the fundamentals of video processing for computer vision tasks putting images into videos. The book concludes with an explanation of the complete model building process for deep learning frameworks using optimized techniques with highlights on model AI explainability. After reading th ...
SQL Server Concurrency
If you've designed your SQL code intelligently and implemented a sensible indexing strategy, there's a good chance your queries will "fly", when tested in isolation. In the real world, however, where multiple processes can access the same data at the same time, SQL Server often has to make one process wait, sacrificing concurrency and performance in order that all processes can succeed without destroying data integrity. Transactions are at the heart of concurrency. I explain their ACID properties, the transaction isolation levels that dictate acceptable behaviors when multiple transactions access the same data simultaneously, and SQL Server's optimistic and pessimistic models for mediating concurrent access. Pessimistic concurrency, SQL Server's default, uses locks to avoid concurrency problems. I explain all the different locks and their compatibility. I show how to control locking with hints and bound connections, and how to troubleshoot excessive blocking and deadlocking. O ...
If you've designed your SQL code intelligently and implemented a sensible indexing strategy, there's a good chance your queries will "fly", when tested in isolation. In the real world, however, where multiple processes can access the same data at the same time, SQL Server often has to make one process wait, sacrificing concurrency and performance in order that all processes can succeed without destroying data integrity. Transactions are at the heart of concurrency. I explain their ACID properties, the transaction isolation levels that dictate acceptable behaviors when multiple transactions access the same data simultaneously, and SQL Server's optimistic and pessimistic models for mediating concurrent access. Pessimistic concurrency, SQL Server's default, uses locks to avoid concurrency problems. I explain all the different locks and their compatibility. I show how to control locking with hints and bound connections, and how to troubleshoot excessive blocking and deadlocking. O ...
Full Stack GraphQL Applications
The GraphQL query language radically reduces over-fetching or under-fetching of data by constructing precise graph-based data requests. In Full Stack GraphQL Applications you'll learn how to build graph-aware web applications that take full advantage of GraphQL's amazing efficiency. Neo4j's William Lyon teaches you everything you need to know to design, deploy, and maintain a GraphQL API from scratch. He reveals how you can build your web apps with GraphQL, React, Apollo, and Neo4j Database, aka "the GRANDstack," to get maximum performance out of GraphQL. The GraphQL API query language radically streamlines data exchanges with backend servers by representing application data as easy-to-understand graphs. You can amplify GraphQL's benefits by using graph-aware tools and data stores, like React, Apollo, and Neo4j, throughout your application. A full stack graph approach provides a consistent data model end to end, reducing friction in data fetching and increasing developer productivit ...
The GraphQL query language radically reduces over-fetching or under-fetching of data by constructing precise graph-based data requests. In Full Stack GraphQL Applications you'll learn how to build graph-aware web applications that take full advantage of GraphQL's amazing efficiency. Neo4j's William Lyon teaches you everything you need to know to design, deploy, and maintain a GraphQL API from scratch. He reveals how you can build your web apps with GraphQL, React, Apollo, and Neo4j Database, aka "the GRANDstack," to get maximum performance out of GraphQL. The GraphQL API query language radically streamlines data exchanges with backend servers by representing application data as easy-to-understand graphs. You can amplify GraphQL's benefits by using graph-aware tools and data stores, like React, Apollo, and Neo4j, throughout your application. A full stack graph approach provides a consistent data model end to end, reducing friction in data fetching and increasing developer productivit ...
100 Go Mistakes and How to Avoid Them
100 Go Mistakes and How to Avoid Them puts a spotlight on common errors in Go code you might not even know you're making. You'll explore key areas of the language such as concurrency, testing, data structures, and more - and learn how to avoid and fix mistakes in your own projects. As you go, you'll navigate the tricky bits of handling JSON data and HTTP services, discover best practices for Go code organization, and learn how to use slices efficiently. Understanding mistakes is the best way to improve the quality of your code. This unique book examines 100 bugs and inefficiencies common to Go applications, along with tips and techniques to avoid making them in your own projects. 100 Go Mistakes and How to Avoid Them shows you how to replace common programming problems in Go with idiomatic, expressive code. In it, you'll explore dozens of interesting examples and case studies as you learn to spot mistakes that might appear in your own applications. Expert author Teiva Harsanyi or ...
100 Go Mistakes and How to Avoid Them puts a spotlight on common errors in Go code you might not even know you're making. You'll explore key areas of the language such as concurrency, testing, data structures, and more - and learn how to avoid and fix mistakes in your own projects. As you go, you'll navigate the tricky bits of handling JSON data and HTTP services, discover best practices for Go code organization, and learn how to use slices efficiently. Understanding mistakes is the best way to improve the quality of your code. This unique book examines 100 bugs and inefficiencies common to Go applications, along with tips and techniques to avoid making them in your own projects. 100 Go Mistakes and How to Avoid Them shows you how to replace common programming problems in Go with idiomatic, expressive code. In it, you'll explore dozens of interesting examples and case studies as you learn to spot mistakes that might appear in your own applications. Expert author Teiva Harsanyi or ...
Python for Data Science
Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You'll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support. You will discover Python's rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket ...
Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You'll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support. You will discover Python's rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket ...
Beginning Spring Boot 3, 2nd Edition
Learn the Spring Boot 3 micro framework and build your first Java-based cloud-native applications and microservices. Spring Boot is the lightweight, nimbler cousin to the bigger Spring Framework, with plenty of "bells and whistles." This updated edition includes coverage of Spring Native, which will help you speed up your Spring Boot applications, as well as messaging with Spring Boot, Spring GraphQL, Spring Data JDBC and reactive relational database connectivity (R2DBC) with SQL. This new edition also covers enhancements to actuator endpoints, MongoDB 4.0 support, layered JAR and WAR support, support to build OCI images using Cloud Native Build Packs, changes to the DataSource initialization mechanism, and how bean validation support has moved to a separate spring-boot-validation-starter module. This book will teach you how to work with relational and NoSQL databases for data accessibility using Spring Boot with Spring Data, how to persist data with the Java Persistence APIs (JPA), ...
Learn the Spring Boot 3 micro framework and build your first Java-based cloud-native applications and microservices. Spring Boot is the lightweight, nimbler cousin to the bigger Spring Framework, with plenty of "bells and whistles." This updated edition includes coverage of Spring Native, which will help you speed up your Spring Boot applications, as well as messaging with Spring Boot, Spring GraphQL, Spring Data JDBC and reactive relational database connectivity (R2DBC) with SQL. This new edition also covers enhancements to actuator endpoints, MongoDB 4.0 support, layered JAR and WAR support, support to build OCI images using Cloud Native Build Packs, changes to the DataSource initialization mechanism, and how bean validation support has moved to a separate spring-boot-validation-starter module. This book will teach you how to work with relational and NoSQL databases for data accessibility using Spring Boot with Spring Data, how to persist data with the Java Persistence APIs (JPA), ...
Machine Learning Engineering on AWS
There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production. This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detai ...
There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production. This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detai ...
Learning Google Analytics
Why is Google Analytics 4 the most modern data model available for digital marketing analytics? Rather than simply reporting what has happened, GA4's new cloud integrations enable more data activation, linking online and offline data across all your streams to provide end-to-end marketing data. This practical book prepares you for the future of digital marketing by demonstrating how GA4 supports these additional cloud integrations. Author Mark Edmondson, Google developer expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations. Data, business, and marketing analysts will learn major facets of GA4's powerful new analytics model, with topics including data architecture and strategy, and data ingestion, storage, and modeling. You'll explore common data activation use cases and get the guidance you need to implement them. ...
Why is Google Analytics 4 the most modern data model available for digital marketing analytics? Rather than simply reporting what has happened, GA4's new cloud integrations enable more data activation, linking online and offline data across all your streams to provide end-to-end marketing data. This practical book prepares you for the future of digital marketing by demonstrating how GA4 supports these additional cloud integrations. Author Mark Edmondson, Google developer expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations. Data, business, and marketing analysts will learn major facets of GA4's powerful new analytics model, with topics including data architecture and strategy, and data ingestion, storage, and modeling. You'll explore common data activation use cases and get the guidance you need to implement them. ...